Fit Point Process Model to Point Pattern on Linear Network
Fit a point process model to a point pattern dataset on a linear network
## S3 method for class 'formula': lppm(X, interaction=NULL, ..., data=NULL)
## S3 method for class 'lpp': lppm(X, ..., eps=NULL, nd=1000)
- Either an object of class
"lpp"specifying a point pattern on a linear network, or a
formulaspecifying the point process model.
- Arguments passed to
- An object of class
"interact"describing the point process interaction structure, or
NULLindicating that a Poisson process (stationary or nonstationary) should be fitted.
- Optional. The values of spatial covariates (other than the Cartesian coordinates) required by the model. A list whose entries are images, functions, windows, tessellations or single numbers.
- Optional. Spacing between dummy points along each segment of the network.
- Optional. Number of dummy points equally spaced along each segment
of the network. Ignored if
This function fits a point process model to data that specify
a point pattern on a linear network. It is a counterpart of
the model-fitting function
to work with objects of class
"lpp" instead of
lppm is generic, with methods for
the first argument
X should be an object of class
(created by the command
lpp) specifying a point pattern
on a linear network.
the first argument is a
formula in the Rlanguage
describing the spatial trend model to be fitted. It has the general form
pattern ~ trend where the left hand side
pattern is usually
the name of a point pattern on a linear network
(object of class
to which the model should be fitted, or an expression which evaluates
to such a point pattern;
and the right hand side
trend is an expression specifying the
spatial trend of the model.
... are passed from
lppm.lpp and from
- An object of class
"lppm"representing the fitted model. There are methods for
coefand similar functions.
Ang, Q.W. (2010) Statistical methodology for events on a network. Master's thesis, School of Mathematics and Statistics, University of Western Australia. Ang, Q.W., Baddeley, A. and Nair, G. (2012) Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591--617.
McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.
example(lpp) lppm(X ~1) lppm(X ~x)